Vibe Coding Best Practices

Aug 22, 2025

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Coding with an AI Copilot: A Practical Guide

Agentic AI tools are no longer a novelty; they are powerful copilots integrated into our daily coding workflows. They can accelerate development, but blindly trusting them can introduce bugs, security flaws, and technical debt. To truly leverage these tools, you must act as the pilot, guiding your AI assistant with precision and skill.

✅ The Do’s: Best Practices for AI Collaboration

1. Be Specific and Provide Context

The most common mistake is giving vague prompts. The AI doesn’t know your project’s architecture or constraints. You must provide that context.

Vague Prompt: "Write a function to get user data."
Specific, Context-Rich Prompt: "Using Python and the Flask-SQLAlchemy extension, write a function that queries the 'User' model by its integer 'id'. The function should return the user object if found, otherwise, it should return a JSON response with a 'message': 'User not found' and a 404 status code."

Resulting Code:

from flask import jsonify
from .models import User

def get_user_by_id(user_id: int):
    """
    Fetches a user by their primary key ID.
    Returns a user object or a 404 JSON response.
    """
    user = User.query.get(user_id)
    if not user:
        return jsonify({"message": "User not found"}), 404
    return user

2. Use AI for Refactoring and Optimization

AI is excellent at recognizing patterns and suggesting more idiomatic or efficient code. Feed it your working but clunky code and ask for improvements.

Original (Before AI): A verbose loop to filter and transform data.

# Find the squares of all even numbers in a list
numbers = [1, 2, 3, 4, 5, 6, 7, 8]
squared_evens = []
for num in numbers:
    if num % 2 == 0:
        squared_evens.append(num * num)
# Result: [4, 16, 36, 64]

Prompt: "Refactor this Python code block to be more concise and Pythonic."

Refactored (After AI): A clean and efficient list comprehension.

numbers = [1, 2, 3, 4, 5, 6, 7, 8]
squared_evens = [num * num for num in numbers if num % 2 == 0]
# Result: [4, 16, 36, 64]

🚫 The Don’ts: Common Pitfalls to Avoid

1. Never Blindly Trust Code, Especially for Security

This is the most critical rule. AI models can inadvertently generate code with major security vulnerabilities. Always review security-sensitive code.

Vulnerable Code Generated by AI: A raw SQL query string, open to SQL Injection.

import sqlite3

def get_user_from_db(user_input):
    conn = sqlite3.connect('database.db')
    cursor = conn.cursor()
    # DANGER: This is vulnerable to SQL injection!
    cursor.execute(f"SELECT * FROM users WHERE username = '{user_input}'")
    return cursor.fetchone()

Your Correction: You must identify this and fix it by using parameterized queries.

import sqlite3

def get_user_from_db(user_input):
    conn = sqlite3.connect('database.db')
    cursor = conn.cursor()
    # CORRECTED: Use a parameterized query to prevent injection
    cursor.execute("SELECT * FROM users WHERE username = ?", (user_input,))
    return cursor.fetchone()

2. Don’t Ignore Your Project’s Conventions

An AI doesn’t know your team’s specific coding style (e.g., variable naming, function length, comment style). It’s your job to enforce consistency.

AI-Generated Code (Ignoring Conventions): The AI might use snake_case for function names when your JavaScript project uses camelCase.
```
// AI might generate this:
function get_user_profile(userId) {
  // ...logic
}
```

Your Correction: Adjust the code to match your project’s style guide for maintainability.

// Corrected to match project style:
function getUserProfile(userId) {
  // ...logic
}

Treat your AI assistant as a brilliant but inexperienced junior developer. Guide it, review its work meticulously, and always retain final ownership of the code.